Field-Enhanced catalysis: Integrating experiment, theory, and machine learning for catalytic innovation

作者：Runze Zhao, Pragyansh Singh, Qiang Li, Jiaqi Yang, Prashant Deshlahra, Hongfu Liu, Fanglin Che · 发表于：Applied Catalysis B: Environmental · 年份：2025 · DOI：10.1016/j.apcatb.2025.125901 · 被引用次数：10 · 研究领域：Machine Learning in Materials Science、Catalytic Processes in Materials Science、Innovative Microfluidic and Catalytic Techniques Innovation

Understanding and controlling the local electric field distributions at catalyst interfaces offers a powerful strategy to modulate reaction kinetics by orders of magnitude, primarily through tuning the electrostatic interactions between polarized reactants and active sites. However, direct measurement of these localized fields with atomic-scale spatial resolution under operando conditions remains an experimental challenge. This perspective provides a critical overview of state-of-the-art techniques for probing local electric fields, including Kelvin Probe Force Microscopy and Vibrational Stark Effect spectroscopy. These approaches are complemented by advanced density functional theory (DFT) method, such as grand canonical DFT and dipole layer-slab models, which enable the simulation of electrostatic potential profiles and prediction of vibrational responses of surface-bound probe molecules under applied fields. Furthermore, we examine the growing role of machine learning (ML), particularly graph neural networks (GNNs) and generative models, in accelerating the prediction of local electric field distributions and the discovery of catalysts tailored for field-enhanced performance. These data-driven models capture complex, nonlinear relationships between catalyst structure, charge redistribution, and electrostatic properties, leveraging physically interpretable descriptors such as effective dipole moments and polarizabilities to predict field-dependent adsorption energetics. The...